Estimation of Prosthetic Knee Angles via Data Fusion of Implantable and Wearable Sensors

In this work, we studied a combination of embedded magnetic measurement system in a knee prosthesis and wearable inertial sensors to estimate two knee joint rotations namely flexion-extension and internal-external rotations. The near optimal sensor configuration was designed for implantable measurement system, and linear estimators were used to estimate the mentioned angles. This system was separately evaluated in a mechanical knee simulator and the effect of the imposed Abduction-Adduction rotation was also studied on the angle estimations. To reduce the power consumption of the internal system, we reduced the sampling rate and duty cycled the implantable sensors. Then we compensated the lack of information via use of kinematic information from wearable sensors to provide accurate angle estimations. As long as this smart prosthesis is not implanted yet on a subject, the angles estimations from implantable sensors and wearable sensors are realistically simulated for four subjects. The simulated angle estimations were fed to the designed data fusion algorithms to boost the estimation performance. The results were considerably improved via use of Maximum Entropy Ordered Weighted Averaging (MEOWA) fusion for flexion angles, but not for internal-external angle estimations.